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Artificial neural networks for energy analysis of office buildings with daylighting

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Cited by:

  1. Jinrong Wu & Su Nguyen & Damminda Alahakoon & Daswin De Silva & Nishan Mills & Prabod Rathnayaka & Harsha Moraliyage & Andrew Jennings, 2024. "A Comparative Analysis of Machine Learning-Based Energy Baseline Models across Multiple Building Types," Energies, MDPI, vol. 17(6), pages 1-18, March.
  2. Jonghoon Ahn, 2020. "Performance Analyses of Temperature Controls by a Network-Based Learning Controller for an Indoor Space in a Cold Area," Sustainability, MDPI, vol. 12(20), pages 1-17, October.
  3. Luca Massidda & Marino Marrocu, 2018. "Smart Meter Forecasting from One Minute to One Year Horizons," Energies, MDPI, vol. 11(12), pages 1-16, December.
  4. Hong, Taehoon & Jeong, Kwangbok & Koo, Choongwan, 2018. "An optimized gene expression programming model for forecasting the national CO2 emissions in 2030 using the metaheuristic algorithms," Applied Energy, Elsevier, vol. 228(C), pages 808-820.
  5. Shen, Meng & Lu, Yujie & Wei, Kua Harn & Cui, Qingbin, 2020. "Prediction of household electricity consumption and effectiveness of concerted intervention strategies based on occupant behaviour and personality traits," Renewable and Sustainable Energy Reviews, Elsevier, vol. 127(C).
  6. Tuukka Salmi & Jussi Kiljander & Daniel Pakkala, 2020. "Stacked Boosters Network Architecture for Short-Term Load Forecasting in Buildings," Energies, MDPI, vol. 13(9), pages 1-15, May.
  7. Wei, Yixuan & Xia, Liang & Pan, Song & Wu, Jinshun & Zhang, Xingxing & Han, Mengjie & Zhang, Weiya & Xie, Jingchao & Li, Qingping, 2019. "Prediction of occupancy level and energy consumption in office building using blind system identification and neural networks," Applied Energy, Elsevier, vol. 240(C), pages 276-294.
  8. Dongjun Suh & Seongju Chang, 2012. "An Energy and Water Resource Demand Estimation Model for Multi-Family Housing Complexes in Korea," Energies, MDPI, vol. 5(11), pages 1-20, November.
  9. Freire, Roberto Zanetti & Mazuroski, Walter & Abadie, Marc Olivier & Mendes, Nathan, 2011. "Capacitive effect on the heat transfer through building glazing systems," Applied Energy, Elsevier, vol. 88(12), pages 4310-4319.
  10. Li, Ning & Xia, Liang & Shiming, Deng & Xu, Xiangguo & Chan, Ming-Yin, 2012. "Dynamic modeling and control of a direct expansion air conditioning system using artificial neural network," Applied Energy, Elsevier, vol. 91(1), pages 290-300.
  11. Alexandru Pîrjan & Simona-Vasilica Oprea & George Căruțașu & Dana-Mihaela Petroșanu & Adela Bâra & Cristina Coculescu, 2017. "Devising Hourly Forecasting Solutions Regarding Electricity Consumption in the Case of Commercial Center Type Consumers," Energies, MDPI, vol. 10(11), pages 1-36, October.
  12. Leung, Philip C.M. & Lee, Eric W.M., 2013. "Estimation of electrical power consumption in subway station design by intelligent approach," Applied Energy, Elsevier, vol. 101(C), pages 634-643.
  13. Olanrewaju, O.A & Jimoh, A.A, 2014. "Review of energy models to the development of an efficient industrial energy model," Renewable and Sustainable Energy Reviews, Elsevier, vol. 30(C), pages 661-671.
  14. Xie Xie & Yang Ni & Tianzi Zhang, 2025. "Machine-Learning-Enhanced Building Performance-Guided Form Optimization of High-Rise Office Buildings in China’s Hot Summer and Warm Winter Zone—A Case Study of Guangzhou," Sustainability, MDPI, vol. 17(9), pages 1-27, May.
  15. Zhao, Hai-xiang & Magoulès, Frédéric, 2012. "A review on the prediction of building energy consumption," Renewable and Sustainable Energy Reviews, Elsevier, vol. 16(6), pages 3586-3592.
  16. Wang, Guochang & Su, Yan & Shu, Lianjie, 2016. "One-day-ahead daily power forecasting of photovoltaic systems based on partial functional linear regression models," Renewable Energy, Elsevier, vol. 96(PA), pages 469-478.
  17. Matallanas, E. & Castillo-Cagigal, M. & Gutiérrez, A. & Monasterio-Huelin, F. & Caamaño-Martín, E. & Masa, D. & Jiménez-Leube, J., 2012. "Neural network controller for Active Demand-Side Management with PV energy in the residential sector," Applied Energy, Elsevier, vol. 91(1), pages 90-97.
  18. Benedetti, Miriam & Cesarotti, Vittorio & Introna, Vito & Serranti, Jacopo, 2016. "Energy consumption control automation using Artificial Neural Networks and adaptive algorithms: Proposal of a new methodology and case study," Applied Energy, Elsevier, vol. 165(C), pages 60-71.
  19. Jeong, Kwangbok & Koo, Choongwan & Hong, Taehoon, 2014. "An estimation model for determining the annual energy cost budget in educational facilities using SARIMA (seasonal autoregressive integrated moving average) and ANN (artificial neural network)," Energy, Elsevier, vol. 71(C), pages 71-79.
  20. Huang, Runya & Huang, Guohe & Cheng, Guanhui & Dong, Cong, 2017. "Regional heuristic interval recourse power system analysis for electricity and environmental systems planning in Eastern China," Resources, Conservation & Recycling, Elsevier, vol. 122(C), pages 185-201.
  21. Orosz, Matthew & Altes-Buch, Queralt & Mueller, Amy & Lemort, Vincent, 2018. "Experimental validation of an electrical and thermal energy demand model for rapid assessment of rural health centers in sub-Saharan Africa," Applied Energy, Elsevier, vol. 218(C), pages 382-390.
  22. Nutkiewicz, Alex & Yang, Zheng & Jain, Rishee K., 2018. "Data-driven Urban Energy Simulation (DUE-S): A framework for integrating engineering simulation and machine learning methods in a multi-scale urban energy modeling workflow," Applied Energy, Elsevier, vol. 225(C), pages 1176-1189.
  23. Ibrahim, Mohamad & Biwole, Pascal Henry & Achard, Patrick & Wurtz, Etienne & Ansart, Guillaume, 2015. "Building envelope with a new aerogel-based insulating rendering: Experimental and numerical study, cost analysis, and thickness optimization," Applied Energy, Elsevier, vol. 159(C), pages 490-501.
  24. Elisa Belloni & Flavia Forconi & Gabriele Maria Lozito & Martina Palermo & Michele Quercio & Francesco Riganti Fulginei, 2025. "Development of Recurrent Neural Networks for Thermal/Electrical Analysis of Non-Residential Buildings Based on Energy Consumptions Data," Energies, MDPI, vol. 18(12), pages 1-21, June.
  25. Pino-Mejías, Rafael & Pérez-Fargallo, Alexis & Rubio-Bellido, Carlos & Pulido-Arcas, Jesús A., 2017. "Comparison of linear regression and artificial neural networks models to predict heating and cooling energy demand, energy consumption and CO2 emissions," Energy, Elsevier, vol. 118(C), pages 24-36.
  26. Olanrewaju, O.A. & Jimoh, A.A. & Kholopane, P.A., 2012. "Integrated IDA–ANN–DEA for assessment and optimization of energy consumption in industrial sectors," Energy, Elsevier, vol. 46(1), pages 629-635.
  27. Proença, Lúcio Costa & Ghisi, Enedir & Tavares, Davi da Fonseca & Coelho, Gabriel Marcon, 2011. "Potential for electricity savings by reducing potable water consumption in a city scale," Resources, Conservation & Recycling, Elsevier, vol. 55(11), pages 960-965.
  28. Zini, Marco & Carcasci, Carlo, 2023. "Machine learning-based monitoring method for the electricity consumption of a healthcare facility in Italy," Energy, Elsevier, vol. 262(PB).
  29. Roth, Jonathan & Rajagopal, Ram, 2018. "Benchmarking building energy efficiency using quantile regression," Energy, Elsevier, vol. 152(C), pages 866-876.
  30. Taesub Lim & Daeung Danny Kim, 2022. "Thermal Comfort Assessment of the Perimeter Zones by Using CFD Simulation," Sustainability, MDPI, vol. 14(23), pages 1-16, November.
  31. Melo, A.P. & Cóstola, D. & Lamberts, R. & Hensen, J.L.M., 2014. "Development of surrogate models using artificial neural network for building shell energy labelling," Energy Policy, Elsevier, vol. 69(C), pages 457-466.
  32. Amasyali, Kadir & El-Gohary, Nora M., 2018. "A review of data-driven building energy consumption prediction studies," Renewable and Sustainable Energy Reviews, Elsevier, vol. 81(P1), pages 1192-1205.
  33. Ibrahim, Mohamad & Wurtz, Etienne & Biwole, Pascal Henry & Achard, Patrick, 2014. "Transferring the south solar energy to the north facade through embedded water pipes," Energy, Elsevier, vol. 78(C), pages 834-845.
  34. Jain, Rishee K. & Smith, Kevin M. & Culligan, Patricia J. & Taylor, John E., 2014. "Forecasting energy consumption of multi-family residential buildings using support vector regression: Investigating the impact of temporal and spatial monitoring granularity on performance accuracy," Applied Energy, Elsevier, vol. 123(C), pages 168-178.
  35. Kim, Wonuk & Jeon, Yongseok & Kim, Yongchan, 2016. "Simulation-based optimization of an integrated daylighting and HVAC system using the design of experiments method," Applied Energy, Elsevier, vol. 162(C), pages 666-674.
  36. García Kerdan, Iván & Morillón Gálvez, David, 2020. "Artificial neural network structure optimisation for accurately prediction of exergy, comfort and life cycle cost performance of a low energy building," Applied Energy, Elsevier, vol. 280(C).
  37. Amasyali, Kadir & El-Gohary, Nora M., 2021. "Real data-driven occupant-behavior optimization for reduced energy consumption and improved comfort," Applied Energy, Elsevier, vol. 302(C).
  38. Wang, Endong, 2015. "Benchmarking whole-building energy performance with multi-criteria technique for order preference by similarity to ideal solution using a selective objective-weighting approach," Applied Energy, Elsevier, vol. 146(C), pages 92-103.
  39. Sunil Kumar Mohapatra & Sushruta Mishra & Hrudaya Kumar Tripathy & Akash Kumar Bhoi & Paolo Barsocchi, 2021. "A Pragmatic Investigation of Energy Consumption and Utilization Models in the Urban Sector Using Predictive Intelligence Approaches," Energies, MDPI, vol. 14(13), pages 1-28, June.
  40. Ascione, Fabrizio & Bianco, Nicola & De Stasio, Claudio & Mauro, Gerardo Maria & Vanoli, Giuseppe Peter, 2017. "Artificial neural networks to predict energy performance and retrofit scenarios for any member of a building category: A novel approach," Energy, Elsevier, vol. 118(C), pages 999-1017.
  41. Buonomano, Annamaria & Calise, Francesco & Palombo, Adolfo, 2012. "Buildings dynamic simulation: Water loop heat pump systems analysis for European climates," Applied Energy, Elsevier, vol. 91(1), pages 222-234.
  42. Venkatraj, V. & Dixit, M.K., 2022. "Challenges in implementing data-driven approaches for building life cycle energy assessment: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 160(C).
  43. Bassamzadeh, Nastaran & Ghanem, Roger, 2017. "Multiscale stochastic prediction of electricity demand in smart grids using Bayesian networks," Applied Energy, Elsevier, vol. 193(C), pages 369-380.
  44. Mahmoud Abdelkader Bashery Abbass & Mohamed Hamdy, 2021. "A Generic Pipeline for Machine Learning Users in Energy and Buildings Domain," Energies, MDPI, vol. 14(17), pages 1-30, August.
  45. Geraldi, Matheus Soares & Ghisi, Enedir, 2022. "Data-driven framework towards realistic bottom-up energy benchmarking using an Artificial Neural Network," Applied Energy, Elsevier, vol. 306(PA).
  46. Cui, X. & Mohan, B. & Islam, M.R. & Chou, S.K. & Chua, K.J., 2017. "Energy performance evaluation and application of an air treatment system for conditioning building spaces in tropics," Applied Energy, Elsevier, vol. 204(C), pages 1500-1512.
  47. Wang, Zeyu & Liu, Jian & Zhang, Yuanxin & Yuan, Hongping & Zhang, Ruixue & Srinivasan, Ravi S., 2021. "Practical issues in implementing machine-learning models for building energy efficiency: Moving beyond obstacles," Renewable and Sustainable Energy Reviews, Elsevier, vol. 143(C).
  48. Machairas, Vasileios & Tsangrassoulis, Aris & Axarli, Kleo, 2014. "Algorithms for optimization of building design: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 31(C), pages 101-112.
  49. Seyedzadeh, Saleh & Pour Rahimian, Farzad & Oliver, Stephen & Rodriguez, Sergio & Glesk, Ivan, 2020. "Machine learning modelling for predicting non-domestic buildings energy performance: A model to support deep energy retrofit decision-making," Applied Energy, Elsevier, vol. 279(C).
  50. Li, Mingchen & Wang, Zhe & Chang, Hao & Wang, Zhoupeng & Guo, Juanli, 2024. "A novel multi-objective generative design approach for sustainable building using multi-task learning (ANN) integration," Applied Energy, Elsevier, vol. 376(PA).
  51. Zihao Li & Daniel Friedrich & Gareth P. Harrison, 2020. "Demand Forecasting for a Mixed-Use Building Using Agent-Schedule Information with a Data-Driven Model," Energies, MDPI, vol. 13(4), pages 1-20, February.
  52. Sehrish Malik & DoHyeun Kim, 2018. "Prediction-Learning Algorithm for Efficient Energy Consumption in Smart Buildings Based on Particle Regeneration and Velocity Boost in Particle Swarm Optimization Neural Networks," Energies, MDPI, vol. 11(5), pages 1-21, May.
  53. Moon Keun Kim & Jaehoon Cha & Eunmi Lee & Van Huy Pham & Sanghyuk Lee & Nipon Theera-Umpon, 2019. "Simplified Neural Network Model Design with Sensitivity Analysis and Electricity Consumption Prediction in a Commercial Building," Energies, MDPI, vol. 12(7), pages 1-13, March.
  54. Ahn, Jonghoon & Cho, Soolyeon, 2017. "Anti-logic or common sense that can hinder machine’s energy performance: Energy and comfort control models based on artificial intelligence responding to abnormal indoor environments," Applied Energy, Elsevier, vol. 204(C), pages 117-130.
  55. Wang, Lan & Lee, Eric W.M. & Yuen, Richard K.K., 2018. "Novel dynamic forecasting model for building cooling loads combining an artificial neural network and an ensemble approach," Applied Energy, Elsevier, vol. 228(C), pages 1740-1753.
  56. Qiu, Changyu & Yi, Yun Kyu & Wang, Meng & Yang, Hongxing, 2020. "Coupling an artificial neuron network daylighting model and building energy simulation for vacuum photovoltaic glazing," Applied Energy, Elsevier, vol. 263(C).
  57. Sung Hoon Yoon & Jonghoon Ahn, 2020. "Comparative Analysis of Energy Use and Human Comfort by an Intelligent Control Model at the Change of Season," Energies, MDPI, vol. 13(22), pages 1-15, November.
  58. Tran, Duc-Hoc & Luong, Duc-Long & Chou, Jui-Sheng, 2020. "Nature-inspired metaheuristic ensemble model for forecasting energy consumption in residential buildings," Energy, Elsevier, vol. 191(C).
  59. Jonghoon Ahn, 2021. "Abatement of the Increases in Cooling Energy Use during a Period of Intense Heat by a Network-Based Adaptive Controller," Sustainability, MDPI, vol. 13(3), pages 1-17, January.
  60. Rakha, Tarek & Nassar, Khaled, 2011. "Genetic algorithms for ceiling form optimization in response to daylight levels," Renewable Energy, Elsevier, vol. 36(9), pages 2348-2356.
  61. Ramya Kuppusamy & Srete Nikolovski & Yuvaraja Teekaraman, 2023. "Review of Machine Learning Techniques for Power Quality Performance Evaluation in Grid-Connected Systems," Sustainability, MDPI, vol. 15(20), pages 1-29, October.
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